Chisholm Rebecca H, Lorenzi Tommaso, Clairambault Jean
School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia.
School of Mathematics and Statistics, University of St Andrews, North Haugh, KY16 9SS, St Andrews, Scotland, United Kingdom. Electronic address: http://www.tommasolorenzi.com.
Biochim Biophys Acta. 2016 Nov;1860(11 Pt B):2627-45. doi: 10.1016/j.bbagen.2016.06.009. Epub 2016 Jun 20.
Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level.
We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies.
Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies.
Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
癌症中的药物诱导耐药性归因于个体细胞或细胞群体水平上的多种生物学机制，这依赖于单细胞水平上表型的随机或表观遗传变化，以及细胞群体水平上肿瘤的适应性。
我们聚焦于肿瘤内异质性，即癌细胞群体中细胞间的变异性，以解释耐药性。为了阐明这种异质性，我们回顾了进化机制，这些机制涵盖了塑造多细胞生物的重大进化过程，以及人类疾病时间尺度上较小的进化窗口。我们还介绍了用于预测癌症耐药性的数学模型以及在联合治疗策略中可规避耐药性的最优控制方法。
癌细胞的可塑性，即单个细胞部分逆转为干细胞样状态并导致癌细胞群体的适应性，可被视为使癌细胞群体对药物损伤产生抗性的逆向进化。这种可逆的可塑性通过数学模型得以体现，这些模型通过连续的表型变量纳入细胞间异质性。此类模型有利于与最优控制方法兼容，用于设计包含细胞毒性和细胞抑制性治疗与表观遗传药物及免疫疗法相结合的优化治疗方案。
利用基于生理学的细胞群体动力学数学模型，从癌症生物学和进化生物学中获取知识，应为肿瘤学家提供设计优化治疗策略以规避耐药性的理论依据，而耐药性仍是癌症治疗的一个主要难题。本文是名为“系统遗传学”特刊的一部分，客座编辑：蔡宇东博士和黄涛博士。
